Can Generative AI Drive India Better?

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Rajneesh De, Consulting Editor, APAC News Network

Generative AI including ML capabilities has emerged as a game changer for the Indian automobile industry. The overall global generative AI market is poised to increase from $11.3 billion in 2023 to $51.8 billion by 2028, growing at a CAGR of 35.6% over this period. The Generative AI in automobile sector is expected to be worth around $2.105 bn by 2032 from $271 Mn in 2022, growing at a CAGR of 23.4% during the forecast period from 2023 to 2032.

Benefits of Generative AI in Automobile

Generative AI enables automobile companies to optimize the vehicle design. This can be done by generating and subsequently evaluating multiple design iterations that are based on specified constraints and objectives. This allows automakers to create more aerodynamic, efficient, and visually appealing vehicles, leading to improved performance and reduced fuel consumption.

Even more crucial is the role of AI in developing autonomous driving systems. The autonomous system helps train models for perception, decision-making, and control. This in turn allows vehicles to interpret their surroundings, make real-time decisions, and navigate autonomously.

The automobile companies can also leverage AI to create realistic simulated environments for testing and validation. Generative AI can predict and optimize maintenance schedules by analyzing sensor data and vehicle performance metrics. The manufacturing set up then identifies potential faults, recommends maintenance actions, and optimizes vehicle uptime while reducing costs associated with unplanned downtime.

There are other AI features too for the automobile players including gesture recognition, voice recognition and control, V2X, and Human Machine interaction (HMI). In addition, self-driving cars, manufactured using AI/ML technologies and AI chips, are also obvious impact of AI in the automotive industry. The upcoming autonomous cars are expected to be safer, smarter, and more capable in all areas.

One of the most potent applications of generative AI in the automotive industry is in vehicle design, specifically simulation and 3D rendering. A perfect example is BMW’s use of AI in its design process.

Generative AI is also revolutionizing predictive maintenance and robotics, two crucial aspects of manufacturing optimization. In robotics, generative AI has been employed to optimize robotic movements on the assembly line, leading to higher-quality outputs and increased manufacturing speed.

Generative AI can optimize the entire supply chain for automotive manufacturers, including procurement, inventory management, and distribution. AI models can generate insights and recommendations for improving supply chain efficiency by analyzing data from suppliers, production processes, and customer demand. Lead times within the supply chain can be optimized by analyzing data from supplier performance, production capabilities, and transportation times.

AI is also helping Automotive players in enhancing the user experience through autonomous driving, ADAS, infotainment, and providing in-car office experience.

Generative AI profoundly impacts autonomous driving, a field that requires the integration and interpretation of vast amounts of data in real-time. It involves creating systems that can recognize and react to static objects like road signs and navigate dynamic scenarios involving pedestrians, cyclists, and other vehicles.

Generative AI is also playing a significant role in enhancing Advanced Driver-Assistance Systems (ADAS). These systems offer features designed to assist drivers and to improve safety, such as collision detection and avoidance, lane keeping assist, adaptive cruise control, and parking assistance.

Market Growth Catalysts

There are three critical factors for the market growth of automotive AI.

Designing and testing: Automobile players can reduce costs and save time by implementing AI for designing and testing. Automotive engineers are using computer simulation models with AI instead of using traditional methods for more productivity and better modeling.

Interest in personalized vehicles: Automobile customers are more inclined towards better user experience than ever. This is precisely where AI contributes the most to success. Customization of AI features and tools is very much possible with the use of AI for manufacturing. The freedom for each customer to personalize their vehicle makes the purchase more interesting. The automakers can predict the needs of their customers by collecting personalized data and producing better-performing vehicles based on various demands.

Better customer experience: The AI-enabled applications installed into vehicles increase performance and this improves the overall experience of customers. There are cars coming up with risk assessment and driver monitoring for accident prevention. This is a great safety measure. By proving next-level safety features using AI, companies are enhancing customer experience and growing better.

AI Use Cases for Automobiles

There has been a collaboration that happened between Qualcomm Technologies and Mercedes-Benz. This partnership is for future Mercedes-Benz vehicles which are set to get advanced digital capabilities using Snapdragon’s solutions. Mercedes-Benz is also actively making level 4/5 autonomous vehicles along with the help of Bosch.

In the case of Mercedes-Benz, ChatGPT further aids its MBUX voice assistant “Hey Mercedes”. It will carry out more complex tasks such as pulling up the menu of an approaching restaurant or asking for real-time navigation inputs, while also recognising speech formats that are more natural and conversational.

Harman International has united with the ARD Audiothek app for creating a vehicle platform that can help automakers design, operate, and maintain the app store.

BMW implemented an AI-based system that incorporates generative design principles. This system takes specific design requirements like weight optimization, connection points, and load capacity. Thereby it generates numerous design alternatives and this results in innovative, efficient, and aesthetically pleasingvehicle parts that fulfill the design criteria.

BMW also launched its latest generation of autonomous driving systems, including BMW Automated Valet Parking and BMW Extended Traffic Jam Assistant. It also introduced BMW introduced the BMW ID, a personal digital profile allowing drivers to save and transfer their preferences, settings, and services across different BMW vehicles. Generative AI algorithms analyze user data to generate personalized recommendations for features, infotainment systems, and user interfaces.

General Motors has integrated AI into its assembly lines. By using generative AI, GM can predict possible machine breakdowns before they occur, optimizing maintenance routines and reducing costly downtime.

Mahindra & Mahindra and CEAT Tyres have recently used generative AI to achieve accurate inventory visibility,data transparency, inventory optimization.

Waymo can employ generative models to create thousands of unique scenarios, replicating a vast range of real-world conditions to train their self-driving algorithms. By using AI to generate these scenarios, Waymo can expose its autonomous systems to diverse driving situations, making them safer and more robust.

Tesla has harnessed the power of generative AI to upgrade its ADAS capabilities. The AI-powered Autopilot system in Tesla vehicles uses generative models to understand and learn from diverse driving situations. The system continuously improves itself by learning from the vast amounts of data generated during Tesla’s daily operations, leading to safer and more efficient ADAS functionalities.

Limitations of AI in Auto

The use of generative AI in the automobile sector significantly depends on collecting and analyzing enormous amounts of data. And this is particularly sensitive and private data. Given the potential severity of any breach or exploitation of this data, this raises questions regarding data privacy and security. Stricter restrictions and customer worries about data protection may hamper the deployment of generative AI in automotive applications.

Generative AI algorithms can potentially create highly realistic synthetic content, such as images and videos. This raises ethical concerns, as the technology can be misused for malicious purposes, including creating deep fakes or misleading visual content. It also presents legal challenges regarding intellectual property rights, copyright infringement, and accountability for generated content.

Deep learning models and other generative AI methods demand a lot of computer resources. These models can be labor- and computationally intensive to train and run, which may restrict their use in real-time in some automobile settings. The requirement for high-performance computing infrastructure might be a barrier, especially in environments with limited resources.